VOL. 10, NO 22, DECEMBER, 2015 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences © 2006-2015 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 10541 MULTI ORIENTATION PERFORMANCE OF FEATURE EXTRACTION FOR HUMAN HEAD RECOGNITION Panca Mudjirahardjo, Rahmadwati, Nanang Sulistiyanto and R. Arief Setyawan Department of Electrical Engineering, Faculty of Engineering, Universitas Brawijaya, Jl. MT. Haryono, Malang, Indonesia E-Mail: panca,rahma}@ub.ac.id ABSTRACT The main component for head recognition is a feature extraction. One of them as our novel method is histogram of transition. In this paper we evaluate multi orientation performance of this feature for human head detection. The input images are head and shoulder image with angle of 315 o , 330 o , 345 o , 15 o , 30 o and 45 o . We use SVM classifier to recognize the input image as a head or non head, which is trained by using normal orientation (0 o ) images. For comparison, we compare the recognition rate with the existing method of feature extraction, i.e. Histogram of Oriented Gradient (HOG) and Linear Binary Pattern (LBP). The experimental results show our feature more robust than the existing feature. Keywords: histogram of transition, head recognition, multi orientation performance. INTRODUCTION Head detection and recognition have been an important research in the last few years. Many applications use this research, such as robotics, automated room monitoring, people counting, person tracking, etc. Many new methods are introduced in this field, to improve the computation time and the recognition rate. One of them is the method based on feature extraction. Feature extraction plays an important role in head recognition. It transforms an original image into a specific vector to be fed into a classifier. An original image cannot be further processed directly. Raw information in an original image does not represent a specific pattern and a machine cannot understand that information. In an image, there are foreground and background patterns. In a simple image, foreground and background pattern can be separated clearly. In a complex image, however, foreground and background pattern cannot be separated clearly. There are many texture patterns both on foreground and background. Sometimes, both foreground and background contain similar texture and color on them. This is a difficult task in a head detection and recognition system. The system has to recognize a foreground pattern as a head or a non-head. Correct choice of a foreground extraction method will increase the recognition rate. A feature is assumed to be able to distinguish a foreground and a background pattern. All of features distinguish a foreground pattern over the background from the edge pattern of the foreground, since a foreground has a specific edge pattern over the background. Currently the most commonly used feature extraction methods are Histogram of Oriented Gradients (HOG)[1][2] and Linear Binary Pattern (LBP) [3]. The new feature extraction is a histogram of transition as our novel method [5][6]. This feature is relied on a background extraction. The simple method to extract a foreground is by using a difference function. Where we label some pixels as foreground pixels, then we calculate all pixel intensity with respect to the foreground pixels. If the difference result is less than or equal to the threshold, then the pixel is consider as foreground, otherwise is as background. The overview of this experiment is shown in Figure-1. The structure of this paper is as follows. Section 2 explains the feature extraction methods. Experimental results are shown in section 3. Finally the paper is concluded in section 4. Figure-1. Overview of the experiment.